Federated learning (FL) is a collaborative learning paradigm for decentralized private data from mobile terminals (MTs). However, it suffers from issues in terms of communication, resource of MTs, and privacy. Existing privacy-preserving FL methods usually adopt the instance-level differential privacy (DP), which provides a rigorous privacy guarantee but with several bottlenecks: severe performance degradation, transmission overhead, and resource constraints of edge devices such as MTs. To overcome these drawbacks, we propose Fed-LTP, an efficient and privacy-enhanced FL framework with \underline{\textbf{L}}ottery \underline{\textbf{T}}icket \underline{\textbf{H}}ypothesis (LTH) and zero-concentrated D\underline{\textbf{P}} (zCDP). It generates a pruned global model on the server side and conducts sparse-to-sparse training from scratch with zCDP on the client side. On the server side, two pruning schemes are proposed: (i) the weight-based pruning (LTH) determines the pruned global model structure; (ii) the iterative pruning further shrinks the size of the pruned model's parameters. Meanwhile, the performance of Fed-LTP is also boosted via model validation based on the Laplace mechanism. On the client side, we use sparse-to-sparse training to solve the resource-constraints issue and provide tighter privacy analysis to reduce the privacy budget. We evaluate the effectiveness of Fed-LTP on several real-world datasets in both independent and identically distributed (IID) and non-IID settings. The results clearly confirm the superiority of Fed-LTP over state-of-the-art (SOTA) methods in communication, computation, and memory efficiencies while realizing a better utility-privacy trade-off.

翻译：联邦学习（FL）是一种针对移动终端（MTs）分散私有数据的协作学习范式。然而，该方法在通信、移动终端资源和隐私方面存在问题。现有隐私保护联邦学习方法通常采用实例级差分隐私（DP），这种方式虽能提供严格的隐私保障，但存在以下瓶颈：性能严重下降、传输开销大以及边缘设备（如移动终端）资源受限。为克服这些缺陷，我们提出Fed-LTP——一种结合**彩票假设**（LTH）与零集中**差分**隐私（zCDP）的高效隐私增强联邦学习框架。该框架在服务端生成剪枝后的全局模型，并在客户端基于zCDP进行从头开始的稀疏到稀疏训练。服务端提出两种剪枝方案：（i）基于权重的剪枝（LTH）确定剪枝后的全局模型结构；（ii）迭代剪枝进一步缩小剪枝模型参数规模。同时，通过基于拉普拉斯机制的模型验证提升Fed-LTP性能。客户端采用稀疏到稀疏训练解决资源受限问题，并提供更严格的隐私分析以降低隐私预算。我们在独立同分布（IID）和非独立同分布（non-IID）设置下，基于多个真实世界数据集评估Fed-LTP有效性。结果明确证实Fed-LTP在通信效率、计算效率和内存效率上均优于现有最优方法，同时实现了更优的效用-隐私权衡。